Transformative Insights into Corrosion Inhibition - A Machine Learning Journey from Prediction to Web-Based Application

Aug 5, 2026

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This study explores and evaluates machine learning models for analyzing expired pharmaceutical data to assess their potential as corrosion inhibitors. The full modeling workflow is deployed through a Streamlit-assisted, web-based platform, enabling practical use by non-programmers without requiring users to retrain models. Models are trained offline to ensure reliable prediction performance and deliver a ready-to-use interface for customization. Among the implemented approaches, XGB achieves the strongest results with an R2-score of 0.99999999, supporting broader accessibility to ML-enabled inhibitor experimentation.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages9

Reading Time23 min

AccessFree to download

Introduction

Background on corrosion and corrosion inhibitors

Limitations of conventional inhibitor evaluation methods

Motivation for machine learning approaches

Related work on ML models for corrosion inhibition efficiency

Method and application goal

Transformative Insights into Corrosion Inhibition: A Machine Learning Journey from Prediction to Web-Based Application Dzaki Asari Surya Putra1, Nicholaus Verdhy Putranto1, Nibras BahyArdyansyah1, Gustina Alfa Trisnapradika1,2, Muhamad Akrom1,2* 1 Study Program in Informatics Engineering, Faculty of Computer Science, Universitas Dian Nuswantoro, Semarang, 50131, Indonesia 2Research Center for Materials Informatics, Faculty of Computer Science, Universitas Dian Nuswantoro, Semarang, 50131, Indonesia *[Correspondence: m.akrom@dsn.dinus.ac.id](Correspondence: m.akrom@dsn.dinus.ac.id) Abstract This study focuses on the exploration and evaluation of machine learning (ML) models to analyze expired pharmaceutical data for their potential use as corrosion inhibitors. Additionally, the entire modeling process is integrated into a user-friendly platform through a Streamlit service-assisted corrosion inhibitor website, facilitating broader accessibility and practical application. The models are trained offline to ensure accurate performance, eliminating the need for users to retrain the models themselves. This approach simplifies the user experience by offering a ready-to-use prediction service directly on the website platform. Among the various ML models implemented, XGB demonstrated the highest performance with an R2-score of 0.99999999. Given that many chemists are not familiar with informatics coding, the researchers developed a Streamlit-based website that includes tools to customize the models. The end product of this work is a corrosion inhibitor experimentation tool that eliminates the need for users to code, making advanced ML techniques accessible to a broader audience within the chemistry community. Keywords: corrosion, inhibitor, machine learning, web, streamlit 1. Introduction Corrosion represents the degradation of metal, characterized by chemical or electrochemical processes occurring between a metal and its surrounding environment, leading to a deterioration in metal quality [1], [2], [3] . Its significance in the environmental context is paramount, and corrosion manifestations can vary depending on the environmental conditions to which a metal is exposed, making consequences challenging to discern. The occurrence of corrosion is influenced by factors such as metal reactivity, degradation, air presence, humidity levels, gas coefficients (e.g., sulfur dioxide and carbon dioxide), and the existence of electrolytes [3] . Corrosion results in losses across diverse sectors, impacting industrial assets, buildings, and various infrastructures [4] . Consequently, the utilization of corrosion inhibitors is imperative to address and mitigate existing corrosion challenges effectively [5],[6]. Corrosion inhibitors play a pivotal practical role, frequently employed to diminish metal losses in the production process and mitigate the potential for material failure [7],[8]. The occurrence of these two events can result in abrupt disruptions in industrial operations, ultimately incurring additional costs. Research indicates that inhibitors, including drugs, play a crucial role in diverse environments and at various concentration levels, exhibiting effectiveness against a range of metals such as zinc, copper, mild steel, aluminum, bronze, and carbon steel. These inhibitors demonstrate the capability to create a protective layer on material surfaces, preventing corrosive reactions between the material and the environment and, consequently, mitigating or reducing corrosion levels in specific materials [9]. Conventional methods for determining the effectiveness of corrosion inhibitors are slow and costly due to the need for chemical experts, materials, and equipment. These methods typically involve experimental techniques such as weight loss assessment, potentiodynamic polarization, and electrochemical impedance spectroscopy [10] . The economic toll of addressing corrosion-related issues amounts to approximately

FAQs

What problem does the research address?

The research addresses the challenge of evaluating corrosion inhibitors efficiently by using machine learning on expired pharmaceutical data to predict corrosion inhibition efficiency (CIE).

How does the proposed solution make the ML workflow easier for users?

The solution integrates trained models into a Streamlit-based website so users can obtain predictions and customize model tools without retraining or coding the models themselves.

Which machine learning model shows the best performance?

XGB shows the highest performance, achieving an R2-score of 0.99999999 in the implemented experiments.

Aug 5, 2026

Upload by Riley